US 12,223,419 B2
Controlling performance of deployed deep learning models on resource constrained edge device via predictive models
Lior Turgeman, Moreshet (IL)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Aug. 26, 2019, as Appl. No. 16/550,290.
Prior Publication US 2021/0064981 A1, Mar. 4, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 8/60 (2018.01); G06N 3/084 (2023.01); G06N 3/10 (2006.01)
CPC G06N 3/08 (2013.01) [G06F 8/60 (2013.01); G06N 3/084 (2013.01); G06N 3/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising a hardware processor to:
receive, at a fog layer used to orchestrate a resource-constrained edge device, a data input and a predicted performance of a deep learning (DL) model deployed on the resource constrained edge device from a predictive model trained to predict a performance characteristic of the deployed DL model on the resource constrained edge device, wherein the predictive model is trained based on training data and output from a performance estimator that estimates a performance of the deployed DL based on the training data and output from the DL model generated on the training data, wherein the predictive model is on the resource constrained edge device and wherein the resource-constrained edge device is not part of the fog layer;
redirect, by the fog layer, the data input to the predictive model;
modify a control input for the deployed DL model based on the data input and the predicted performance; and
send, by the fog layer, the modified control input to the deployed DL model to modify performance of the deployed DL model on the resource constrained edge device.